Approaches to Phase 1 Clinical Trial Design Focused on Safety, Efficiency, and Selected Patient Populations: A Report from the Clinical Trial Design Task Force of the National Cancer Institute Investigational Drug Steering Committee
Bibliographic record
Abstract
The goals and objectives of phase 1 clinical trials are changing to include further evaluation of endpoints such as molecular targeted effects, in addition to dose-toxicity profile of the investigational agent. Because of these changes in focus, the National Cancer Institute and Investigational Drug Steering Committee's Task Force on Clinical Trial Design met to evaluate the most efficient ways to design and implement early clinical trials with novel therapeutics. Clinical approaches discussed included the conventional 3 + 3 cohort expansion phase 1 design, multi-institutional phase 1 studies, accelerated titration designs, continual reassessment methods, the study of specific target patient populations, and phase 0 studies. Each of these approaches uniquely contributes to some aspect of the phase 1 study, with all focused on dose and schedule determination, patient safety, and limited patient exposure to ineffective doses of investigational agent. The benefit of labor-intensive generation of preliminary biomarker evidence of target inhibition, as well as the value of molecular profiling of the study population, is considered. New drug development is expensive and the failure rate remains high. By identifying patient populations expected to respond to the study agent and tailoring the treatment with a novel drug, investigators will be one step closer to personalizing cancer treatment. The "fail early and fast" approach is acceptable if the appropriate patient population is evaluated in the phase 1 trial. The approaches outlined in this overview address the merits, advantages, disadvantages, and obstacles encountered during first in human studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.363 | 0.207 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.020 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".